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Multimodal attentions: collaborative ethnographic remixes in the era of AI 
Dominic Boyer (Rice University)
Cymene Howe (Rice University)
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Dominic Boyer (Rice University)
Cymene Howe (Rice University)
Traditional Open Panel

Short Abstract:

In dialogue with contemporary AI techniques, this panel explores the possibilities of collaborative multimodal ethnography to connect different fieldsites and fieldknowledges through the experimental practice of remixing ethnographic artifacts.

Long Abstract:

Both multimodality and collaboration have become transformational vectors in the epistemic practices of STS, anthropology and related human sciences. This panel explores the possibilities of collaborative multimodal ethnography. Typically, multimodal collaborations occur within the context of ethnographic research projects undertaken by artist-ethnographers (e.g., the Ethnographic Terminalia Collective) or where artistic collaborators come into dialogue with ethnographers to help them realize new expressive potentials (e.g., Anna Tsing’s Golden Snail Opera). In both cases, projects are grounded in direct field research experience with media projects broadcast outwards. Building upon these works, this panel wonders what could happen if ethnographic multimodality were to become more participatory and open-ended. What would happen, for example, if multimodal ethnography were opened up to collaborations beyond those intimately familiar with the original fieldwork situation? What if the multimodal expressions of different fieldsites and fieldknowledges came into dialogue with one another? The experimental idea that guides this panel is that of the remix, a multimodal technique with a deep and diverse history in expressive practice. Through remix experiments, collaborators can take their partners' ethnographic artifacts and "lay down tracks" to create new ethnographic assemblages—for example, an essay becomes a play, or a film becomes a painting—that supplement and reimagine their originals with attentions and concerns originating from a different field situation. Could an artist-ethnographer discover something new in their own research by engaging its remix? We undertake this experiment in full awareness that AI technologies like large language models (LLM) offer automated, extractivist remix strategies that are becoming very popular prosthetics for creating seemingly unique text and image assemblages. Some of the remix practices we envision engage AI techniques, others are more artisanal and experiential in form.

Accepted papers:

Session 1